clean code
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@ -36,7 +36,7 @@ class PyTorchMLPModel(nn.Module):
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"""
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def __init__(self, input_dim: int, output_dim: int, **kwargs):
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super(PyTorchMLPModel, self).__init__()
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super().__init__()
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hidden_dim: int = kwargs.get("hidden_dim", 256)
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dropout_percent: int = kwargs.get("dropout_percent", 0.2)
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n_layer: int = kwargs.get("n_layer", 1)
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@ -65,7 +65,7 @@ class Block(nn.Module):
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"""
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def __init__(self, hidden_dim: int, dropout_percent: int):
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super(Block, self).__init__()
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super().__init__()
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self.ff = FeedForward(hidden_dim)
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self.dropout = nn.Dropout(p=dropout_percent)
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self.ln = nn.LayerNorm(hidden_dim)
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@ -85,7 +85,7 @@ class FeedForward(nn.Module):
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"""
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def __init__(self, hidden_dim: int):
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super(FeedForward, self).__init__()
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super().__init__()
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self.net = nn.Sequential(
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nn.Linear(hidden_dim, hidden_dim),
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nn.ReLU(),
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@ -229,7 +229,6 @@ def test_extract_data_and_train_model_Classifiers(mocker, freqai_conf, model):
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}
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})
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if freqai.dd.model_type == 'joblib':
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model_file_extension = ".joblib"
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elif freqai.dd.model_type == "pytorch":
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